2025
From Requirement to Solution: Unveiling Problem-Driven Design Patterns in Visual Analytics
Yuchen Wu, Shenghan Gao, Shizhen Zhang, Xiaofeng Dou, Xingbo Wang, Quan Li
IEEE Transactions on Visualization and Computer Graphics (TVCG) 2025 TVCG 2025
We formulated refined topologies for data, requirements, and solutions. We propose conceptualizing the connections between requirements, data, and solutions through knowledge graphs and utilizing solution paths to encapsulate fundamental problem-solving knowledge in visual analytics research. Through the consolidation of solution paths into a graph and analyzing their interconnections, we discerned a subset of problem-driven design patterns that demonstrated the efficacy of our approach.
2024
Trinity: Synchronizing Verbal, Nonverbal, and Visual Channels to Support Academic Oral Presentation Delivery
Yuchen Wu, Shengxin Li, Shizhen Zhang, Xingbo Wang, Quan Li
International Symposium of Chinese CHI 2024 ChineseCHI 2024Best Paper
We introduce Trinity, a hybrid mobile-centric delivery support system that provides guidance for multichannel delivery on-the-fly. On the desktop side, Trinity facilitates script refinement and offers customizable delivery support based on large language models (LLMs). Based on the desktop configuration, Trinity App enables a remote mobile visual control, multi-level speech pace modulation, and integrated delivery prompts for synchronized delivery.
KMTLabeler: An Interactive Knowledge-Assisted Labeling Tool for Medical Text Classification
He Wang, Yang Ouyang, Yuchen Wu, Chang Jiang, Lixia Jin, Yuanwu Cao, Quan Li
IEEE Transactions on Visualization and Computer Graphics (TVCG) 2024 TVCG 2024
we present a collaborative human-ML teaming workflow that seamlessly integrates visual cluster analysis and active learning to assist domain experts in labeling medical text with high efficiency. An innovative embedding network is introduced to incorporates expert insights to generate task-specific embeddings for medical texts. We integrate the workflow and embedding network into a visual analytics tool named KMTLabeler, equipped with coordinated multi-level views and interactions.
2023
Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning
Yang Ouyang, Yuchen Wu, He Wang, Chenyang Zhang, Furui Cheng, Chang Jiang, Lixia Jin, Yuanwu Cao, Quan Li
IEEE Transactions on Visualization and Computer Graphics (TVCG) 2023 VIS 2023
We present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients.
LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce
Yuchen Wu, Yuansong Xu, Shenghan Gao, Xingbo Wang, Wenkai Song, Zhiheng Nie, Xiaomeng Fan, Quan Li
IEEE Transactions on Visualization and Computer Graphics (TVCG) 2023 VIS 2023
This study identified computational features, formulated design requirements, and developed LiveRetro , an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels.